A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study

Abstract

Background: Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC.

Methods: 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300).

Findings: The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739-0.920), 0.804 (95% CI, 0.732-0.877), and 0.827 (95% CI, 0.755-0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05).

Interpretation: A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.

Keywords: AIC, Akaike information criterion; CT, computed tomography; DCA, decision curve analysis; DFS, disease free survival; DLRN, deep learning radiomics nomogram; Deep learning; GR, good response; ICC, interclass correlation coefficient; IDI, integrated discrimination improvement; LAGC, locally advanced gastric cancer; LASSO, least absolute shrinkage and selection operator; Locally advanced gastric cancer; NACT, neoadjuvant chemotherapy; NRI, Net reclassification index; Neoadjuvant chemotherapy; PR, poor response; ROC, Receiver operating characteristic; ROI, regions of interest; Radiomics nomogram; TRG, tumor regression grade.

Figure 1

Workflow of the study. Workflow of deep learning radiomics nomogram (DLRN) modeling for good response (GR) prediction in patients with locally advanced gastric cancer (LAGC). CT, computed tomography.

Figure 2

Deep learning radiomics nomogram (DLRN) and its performance. (A) DLRN with the handcrafted and deep learning signatures and clinical T stage. (B) Box plots showing patterns of correlation between therapeutic response and DLRN score for in the TC, IVC, EVC1, and EVC2, respectively. (C) Calibration curves of DLRN in all the four cohorts. (D) Decision curve analysis for DLRN, deep learning signature, handcrafted signature, and clinical model.

Figure 3

Receiver operating characteristic (ROC) curves of the four models. ROC curves of DLRN, deep learning signature, handcrafted signature, and clinical model, for predicting good responder (GR) in the (A) training cohort, (B) internal validation cohort, (C) external validation cohort 1, and (D) external validation cohort 2, respectively.

Figure 4

Kaplan-Meier curves and forest plot of Disease-free survival (DFS) on the follow-up LAGC cohort. (A) Kaplan–Meier curves of DFS between the groups with low and high DLRN scores in the follow-up cohort. (B) Forest plot illustrating multivariable Cox regression analyses for DFS in the follow-up cohort.